Advances in Computer Simulation

1. Front Matter

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2. Introduction to Monte Carlo algorithms

   • Werner Krauth

   Pages 1-35

3. Cluster algorithms

   • Ferenc Niedermayer

   Pages 36-49

4. Optimized monte carlo methods

   • Enzo Marinari

   Pages 50-81

5. Monte Carlo on parallel and vector computers

   • Dietrich Stauffer

   Pages 82-87

6. Error estimates on averages of correlated data

   • Henrik Flyvbjerg

   Pages 88-103

7. Stochastic differential equations

   • Dietrich E. Wolf

   Pages 104-121

8. Frustrated systems: Ground state properties via combinatorial optimization

   • Heiko Rieger

   Pages 122-158

9. Molecular dynamics

   • Hans Herrmann

   Pages 159-166

Computer simulation has become a basic tool in many branches of physics such as statistical physics, particle physics, or materials science. The application of efficient algorithms is at least as important as good hardware in large-scale computation. This volume contains didactic lectures on such techniques based on physical insight. The emphasis is on Monte Carlo methods (introduction, cluster algorithms, reweighting and multihistogram techniques, umbrella sampling), efficient data analysis and optimization methods, but aspects of supercomputing, the solution of stochastic differential equations, and molecular dynamics are also discussed. The book addresses graduate students and researchers in theoretical and computational physics.

• Monte Carlo
• Monte Carlo methods
• Simulation
• computer simulation
• molecular dynamics
• optimization
• stochastic differential equations

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